added sparse_softmax_cross_entropy_with_logits

Oceania2018 · Oceania2018 · commit a4f03c22ec63 · 2019-06-05T07:28:27.000-05:00
diff --git a/src/TensorFlowNET.Core/APIs/tf.nn.cs b/src/TensorFlowNET.Core/APIs/tf.nn.cs
@@ -90,6 +90,17 @@ public static Tensor bias_add(Tensor value, RefVariable bias, string data_format
             public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
                 => gen_nn_ops.softmax(logits, name);
 
+            /// <summary>
+            /// Computes sparse softmax cross entropy between `logits` and `labels`.
+            /// </summary>
+            /// <param name="labels"></param>
+            /// <param name="logits"></param>
+            /// <param name="name"></param>
+            /// <returns></returns>
+            public static Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = null,
+            Tensor logits = null, string name = null)
+                => nn_ops.sparse_softmax_cross_entropy_with_logits(labels: labels, logits: logits, name: name);
+
             public static Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)
                 => nn_ops.softmax_cross_entropy_with_logits_v2_helper(labels, logits, axis: axis, name: name);
         }
diff --git a/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs b/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
@@ -203,6 +203,27 @@ public Graph BuildGraph()
                 var h_drop = tf.nn.dropout(h_pool_flat, keep_prob);
             });
 
+            Tensor logits = null;
+            Tensor predictions = null;
+            with(tf.name_scope("output"), delegate
+            {
+                logits = tf.layers.dense(h_pool_flat, keep_prob);
+                predictions = tf.argmax(logits, -1, output_type: tf.int32);
+            });
+
+            with(tf.name_scope("loss"), delegate
+            {
+                var sscel = tf.nn.sparse_softmax_cross_entropy_with_logits(logits: logits, labels: y);
+                var loss = tf.reduce_mean(sscel);
+                var optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step: global_step);
+            });
+
+            with(tf.name_scope("accuracy"), delegate
+            {
+                var correct_predictions = tf.equal(predictions, y);
+                var accuracy = tf.reduce_mean(tf.cast(correct_predictions, TF_DataType.TF_FLOAT), name: "accuracy");
+            });
+
             return graph;
         }
 
